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Deep Convolution Neural Network Motor Fault Identification Based on Generative Adversarial Network under Unbalanced Sample
2020
DEStech Transactions on Engineering and Technology Research
Based on the problem that the traditional motor fault diagnosis method relies on the signal processing power and the model generalization ability is poor, this paper proposes a fault diagnosis method based on generative adversarial network under unbalanced data sets. It builds a small sample training set to train generative adversarial network, and adds the generated sample to the original small sample training set. A deep convolutional neural network (DCNN) model that is suitable for motor
doi:10.12783/dtetr/amee2019/33455
fatcat:ddjjamkcq5bxbdp4zoao2jpozu